基于城市用地的公共自行车骑行量建模

唐炉亮; 陈西; 杨雪; 张霞; 李清泉

doi:10.19818/j.cnki.1671-1637.2018.01.014

基于城市用地的公共自行车骑行量建模

doi: 10.19818/j.cnki.1671-1637.2018.01.014

唐炉亮^1,,
陈西¹,
杨雪¹,
张霞²,
李清泉^{1, 3}

1.
武汉大学测绘遥感信息工程国家重点实验室机构, 湖北武汉 430079
2.
武汉大学城市设计学院, 湖北武汉 430070
3.
深圳大学机构土木工程学院, 广东深圳 518060

基金项目:

国家重点研发计划 2017YFB0503604

国家重点研发计划 2016YFE0200400

国家自然科学基金项目 41271442

国家自然科学基金项目 41571430

国家自然科学基金项目 41671442

教育部联合基金项目 6141A02022341

详细信息

作者简介:
唐炉亮(1973-), 男, 湖南湘潭人, 武汉大学教授, 工学博士, 从事时空地理信息分析研究

中图分类号: U491.1
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- 被引次数: 0
出版历程
- 收稿日期: 2017-09-06
- 刊出日期: 2018-02-25

Public bicycle usage modeling based on urban land use

1.
State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan 430079, Hubei, China
2.
School of Urban Design, Wuhan University, Wuhan 430070, Hubei, China
3.
College of Civil Engineering, Shenzhen University, Shenzhen 518060, Guangdong, China

More Information

Author Bio:
TANGLu-liang(1973-), male, professor, PhD, till@whu.edu.cn

摘要

摘要: 将公共自行车出行模式划分为前往站点、租车骑行和还车离开3个阶段, 分别研究了城市用地在每个阶段对公共自行车骑行量的影响; 针对第1、3阶段, 采用兴趣点度量公共自行车站点周围的城市用地类型, 通过引入时间满意度函数, 基于多元回归分析, 提出了预测骑行产生量和吸引量的建模方法; 针对第2阶段, 构建了城市用地对公共自行车骑行量的6个影响指标, 包括起始站点骑行产生量、起始站点分布密度、起始站点偏离度、终止站点骑行吸引量、起终站点间距与起终站点用地差异; 对纽约市961 865条以及上海市1 185 816条公共自行车骑行记录进行多元回归分析。分析结果表明: 在保持用地因素数量不变的情况下, 传统方法针对纽约和上海记录数据的决定系数分别为0.581、0.474, 建模方法的决定系数较高, 分别达到了0.738、0.607;针对自行车站点间客流量的建模, 提出的6个指标均对站点间客流量有显著影响, 并且模型的调整决定系数为0.487。可见, 建模方法更加合理地衡量了城市用地对公共自行车骑行量的影响, 并对站点的骑行产生量、站点间客流量与站点吸引量实现了更为准确的建模。
- 交通信息工程 /
- 公共自行车骑行量 /
- 时间满意度函数 /
- 城市用地 /
- 出行模式 /
- 多元回归
Abstract: The public bicycle travel pattern was divided into three phases, namely going to a station, riding after renting, and leaving after returning.The influence of urban land use on public bicycle usage was investigated for each phase.For the first and third phases, the points of interst were used to measure the type of urban land use surrounding public bicycle station.By introducing time satisfaction function, a modeling method of the prediction of public bicycle demand and attraction was proposed based on multiple regression analysis.For the second phase, six influence factors of public bicycle usage related to urban land use were established, including the bicycle demand of origin station, the density of origin station, the remoteness of origin station, the attraction of destination station, the distance between origin and destination station, and the land use difference between origin and destination station.961 865 and 1 185 816 records of public bicycle usage respectively from New York and Shanghai were analyzed by using multipleregression analysis.Analysis result indicates that for the records of New York and Shanghai, the modeling method yields higher determination coefficients (0.738 and 0.607) than the traditional method (0.581 and 0.474) when the number of land use factors is invariable.All of six proposed factors have significant influence on the traffic volumes between the stations in bicycle traffic volume model, and the adjusted determination coefficient is 0.487.In conclusion, the modeling method can measure the influence of urban land use on the public bicycle usage more feasibly, and it provides a more accurate model of trip demand at stations, traffic volume between stations, and attraction of stations for public bicycle trips.
- traffic information engineering /
- public bicycle usage /
- time satisfaction function /
- urban land use /
- travel pattern /
- multiple regression

HTML全文

图 1 公共自行车出行的3个阶段

Figure 1. Three stages of public bicycle travel

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图 2 POI和自行车站点分布

Figure 2. Distribution of POIs and public bicycle stations

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图 3 线性时间满意度函数曲线

Figure 3. Curve of linear time satisfaction function

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图 4 凹凸时间满意度函数曲线

Figure 4. Curves of convex/concave time satisfaction function

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图 5 余弦分布时间满意度函数曲线

Figure 5. Curve of cosine distributed time satisfaction function

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图 6 降指数Sigmoid时间满意度函数曲线

Figure 6. Curves of descending exponential Sigmoid time satisfaction function

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图 7 骑行距离与频率关系

Figure 7. Relationship between travel distance and frequency

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表 1 不同时间满意度函数分析结果对比

Table 1. Comparison of analysis results of different time satisfaction functions

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表 2 不同L和U取值分析结果对比

Table 2. Comparison of analysis results with different values of L and U

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表 3 不同方法分析结果对比

Table 3. Comparison of analysis results of different methods

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表 4 纽约用地因素回归分析结果

Table 4. Regression analysis result of land use factors of New York

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表 5 站点间客流量计算结果

Table 5. Calculation result of traffic volume between stations

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